{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "63f19c9e",
   "metadata": {},
   "source": [
    "# Detecção de Embarcações em Ambientes Marinhos Usando Deep Learning Aplicado à Bioacústica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e796c2c9",
   "metadata": {},
   "source": [
    "## Importando todas as Libs que serão necessárias "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "22806c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "import os\n",
    "import numpy as np\n",
    "import h5py\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout , BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "import librosa\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d92dd3",
   "metadata": {},
   "source": [
    "## Definindo Constantes e funções auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "edff6c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# === CONFIGURAÇÕES ===\n",
    "INPUT_SHAPE = (513, 27, 1)\n",
    "PATH_BARCO = '../pxx_barcos'\n",
    "PATH_NORMAL = '../pxxs_normais'\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c83f25f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(SEED)\n",
    "random.seed(SEED)\n",
    "tf.random.set_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d841f6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def augment_noise(data, std=0.01, copies=3, seed=42):\n",
    "    \"\"\"\n",
    "    Realiza data augmentation adicionando ruído gaussiano às matrizes de entrada.\n",
    "\n",
    "    Esta função recebe uma lista de matrizes (por exemplo, espectrogramas ou Mel Spectrograms)\n",
    "    e retorna uma lista aumentada, onde para cada matriz original são geradas\n",
    "    'copies' versões com ruído gaussiano adicionado. Útil para aumentar\n",
    "    a variabilidade do dataset e ajudar na generalização do modelo.\n",
    "\n",
    "    Parâmetros\n",
    "    ----------\n",
    "    data : list or array-like\n",
    "        Lista de matrizes de entrada a serem aumentadas (shape ex: [N, 128, 27]).\n",
    "\n",
    "    std : float, opcional (default=0.01)\n",
    "        Desvio padrão do ruído gaussiano a ser adicionado.\n",
    "\n",
    "    copies : int, opcional (default=3)\n",
    "        Número de cópias aumentadas a serem geradas para cada matriz original.\n",
    "\n",
    "    seed : int, opcional (default=42)\n",
    "        Semente para o gerador de números aleatórios, garantindo reprodutibilidade.\n",
    "\n",
    "    Retorno\n",
    "    -------\n",
    "    augmented : list\n",
    "        Lista contendo todas as cópias aumentadas com ruído.\n",
    "\n",
    "    Exemplo\n",
    "    -------\n",
    "    >>> augmented_data = augment_noise(mel_spectrograms, std=0.02, copies=5)\n",
    "    >>> print(len(augmented_data))  # Deve ser igual a len(mel_spectrograms) * 5\n",
    "    \"\"\"\n",
    "    augmented = []\n",
    "    rng = np.random.default_rng(seed)\n",
    "    for sample in data:\n",
    "        for _ in range(copies):\n",
    "            noisy = sample + rng.normal(0, std, sample.shape)\n",
    "            augmented.append(noisy)\n",
    "    return augmented\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e46c81b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def carregar_mel_h5(path, label, sr=8000, n_fft=1024, n_mels=128):\n",
    "    \"\"\"\n",
    "    Carrega matrizes Pxx de arquivos .h5 e converte para Mel Spectrograms prontos para CNN.\n",
    "\n",
    "    Esta função percorre todos os arquivos .h5 em um diretório fornecido, extrai a matriz 'pxx' de cada arquivo,\n",
    "    projeta a matriz de densidade espectral para a escala Mel utilizando um banco de filtros Mel,\n",
    "    converte para escala dB e redimensiona para shape fixo. Ideal para pipelines de Deep Learning\n",
    "    com entradas espectrais.\n",
    "\n",
    "    Parâmetros\n",
    "    ----------\n",
    "    path : str\n",
    "        Caminho para a pasta onde estão os arquivos .h5 com a matriz 'pxx' salva.\n",
    "\n",
    "    label : int\n",
    "        Rótulo (classe) a ser atribuído para todas as matrizes carregadas deste diretório (ex: 0=normal, 1=barco).\n",
    "\n",
    "    sr : int, opcional (default=8000)\n",
    "        Taxa de amostragem em Hz utilizada para construir o banco de filtros Mel.\n",
    "\n",
    "    n_fft : int, opcional (default=1024)\n",
    "        Tamanho da janela FFT utilizada na geração das matrizes Pxx.\n",
    "\n",
    "    n_mels : int, opcional (default=128)\n",
    "        Número de bandas Mel para a projeção.\n",
    "\n",
    "    Retorno\n",
    "    -------\n",
    "    data : list of np.ndarray\n",
    "        Lista de Mel Spectrograms convertidos, já redimensionados e prontos para uso em CNN\n",
    "        (shape de cada item: [128, 27, 1]).\n",
    "\n",
    "    labels : list of int\n",
    "        Lista de rótulos, um para cada matriz carregada.\n",
    "\n",
    "    Exemplo\n",
    "    -------\n",
    "    >>> X, y = carregar_mel_h5('./pxx_barcos', label=1)\n",
    "    >>> print(X[0].shape)  # (128, 27, 1)\n",
    "    >>> print(y[:5])       # [1, 1, 1, 1, 1]\n",
    "    \"\"\"\n",
    "    data = []\n",
    "    labels = []\n",
    "    mel_basis = librosa.filters.mel(sr=sr, n_fft=n_fft, n_mels=n_mels)\n",
    "\n",
    "    for file in os.listdir(path):\n",
    "        if file.endswith(\".h5\"):\n",
    "            file_path = os.path.join(path, file)\n",
    "            with h5py.File(file_path, 'r') as h5_file:\n",
    "                pxx = np.array(h5_file['pxx'])\n",
    "\n",
    "                if pxx.shape[0] != mel_basis.shape[1]:\n",
    "                    continue  # pula arquivos incompatíveis\n",
    "\n",
    "                mel_pxx = np.dot(mel_basis, pxx)\n",
    "                mel_db = librosa.power_to_db(mel_pxx, ref=np.max)\n",
    "\n",
    "                # Redimensiona para CNN\n",
    "                mel_db = np.resize(mel_db, (128, 27))\n",
    "                data.append(mel_db[..., np.newaxis])  # shape (128, 27, 1)\n",
    "                labels.append(label)\n",
    "\n",
    "    return data, labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed81c9eb",
   "metadata": {},
   "source": [
    "## Carregando os dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "462ed2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregamento\n",
    "dados_barcos, rotulos_barcos = carregar_mel_h5(PATH_BARCO, label=1)\n",
    "dados_normais, rotulos_normais = carregar_mel_h5(PATH_NORMAL, label=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4e34be26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pegue uma amostra de cada classe\n",
    "mel_normal = dados_normais[0]  # shape (128, 27, 1)\n",
    "mel_barco = dados_barcos[0]    # shape (128, 27, 1)\n",
    "\n",
    "# Remover o eixo extra se necessário\n",
    "if mel_normal.ndim == 3:\n",
    "    mel_normal = mel_normal.squeeze()\n",
    "if mel_barco.ndim == 3:\n",
    "    mel_barco = mel_barco.squeeze()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Frequências Mel para os labels do eixo y\n",
    "mel_f = librosa.mel_frequencies(n_mels=128, fmin=0, fmax=8000//2)\n",
    "extent = [0, 9, mel_f[0], mel_f[-1]]\n",
    "\n",
    "# Plot Mel Normal\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.imshow(mel_normal, aspect='auto', origin='lower', cmap='jet', extent=extent)\n",
    "plt.title(\"Mel Spectrogram - Áudio Normal\")\n",
    "plt.ylabel(\"Frequência (Hz)\")\n",
    "plt.xticks(np.arange(0, 10, 1))\n",
    "plt.yticks(np.linspace(mel_f[0], mel_f[-1], 8).astype(int))\n",
    "plt.xlabel(\"Tempo (min)\")\n",
    "\n",
    "# Plot Mel Barco\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.imshow(mel_barco, aspect='auto', origin='lower', cmap='jet', extent=extent)\n",
    "plt.title(\"Mel Spectrogram - Áudio com Barco\")\n",
    "plt.ylabel(\"Frequência (Hz)\")\n",
    "plt.xticks(np.arange(0, 10, 1))\n",
    "plt.yticks(np.linspace(mel_f[0], mel_f[-1], 8).astype(int))\n",
    "plt.xlabel(\"Tempo (min)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f18d1d5",
   "metadata": {},
   "source": [
    "### Aumentando os dados com ruido Gaussiano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1a7a8fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aumenta classe barco\n",
    "dados_barcos_aug = augment_noise(dados_barcos, std=0.01, copies=3)\n",
    "rotulos_barcos_aug = [1] * len(dados_barcos_aug)\n",
    "\n",
    "# Junta tudo\n",
    "X = np.array(dados_normais + dados_barcos + dados_barcos_aug)\n",
    "y = np.array(rotulos_normais + rotulos_barcos + rotulos_barcos_aug)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f60cb27",
   "metadata": {},
   "source": [
    "### Dividindo dados em Treino e Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d1b7d259",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, stratify=y, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f47cb41",
   "metadata": {},
   "source": [
    "### Aplicando Peso por Classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "07198d71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pesos de classe\n",
    "classes = np.unique(y_train)\n",
    "class_weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train)\n",
    "class_weights_dict = dict(zip(classes, class_weights))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c4d5e1",
   "metadata": {},
   "source": [
    "### Explicação de Data Augmentation (noise) + compute_class_weight\n",
    "O uso **simultâneo de Data Augmentation (noise) e de `compute_class_weight`** não é redundante. Eles atuam em **frentes diferentes** e **se complementam** para melhorar a robustez do modelo, especialmente em datasets desbalanceados e pequenos. Veja a explicação detalhada:\n",
    "\n",
    "---\n",
    "\n",
    "#### **1. Augmentation com Noise**\n",
    "\n",
    "```python\n",
    "dados_barcos_aug = augment_noise(dados_barcos, std=0.01, copies=3)\n",
    "rotulos_barcos_aug = [1] * len(dados_barcos_aug)\n",
    "```\n",
    "\n",
    "**Objetivo:**\n",
    "\n",
    "* **Aumentar o número de amostras da classe minoritária** (\"barco\") no dataset de treino.\n",
    "* Introduzir **variação artificial** nos exemplos de barco, dificultando o overfitting (o modelo não vai só “decorar” os exemplos originais).\n",
    "* Aumentar a **diversidade** do conjunto de treino, o que **melhora a capacidade de generalização** da CNN.\n",
    "\n",
    "---\n",
    "\n",
    "#### **2. Uso de `compute_class_weight`**\n",
    "\n",
    "```python\n",
    "classes = np.unique(y_train)\n",
    "class_weights = compute_class_weight(class_weight='balanced', classes=classes, y=y_train)\n",
    "class_weights_dict = dict(zip(classes, class_weights))\n",
    "```\n",
    "\n",
    "**Objetivo:**\n",
    "\n",
    "* **Ajustar o “peso” da penalidade de erro no cálculo da loss** durante o treinamento.\n",
    "* Se mesmo após o augmentation a classe 1 (“barco”) continuar com menos exemplos, os erros dela vão pesar mais no ajuste dos parâmetros da rede.\n",
    "* Garante que **o modelo não “ignore” a classe minoritária**, mesmo que ela seja menos frequente.\n",
    "* Isso é especialmente importante em casos em que:\n",
    "\n",
    "  * O augmentation não igualou exatamente as quantidades,\n",
    "  * O shuffle ou split acabou deixando a base de treino desbalanceada,\n",
    "  * Você deseja garantir imparcialidade mesmo em experimentos futuros.\n",
    "\n",
    "---\n",
    "\n",
    "#### **Por que os dois?**\n",
    "\n",
    "* O **augmentation** resolve parte do desbalanceamento e **aumenta a variabilidade do dado**, mas pode não garantir 100% de equilíbrio absoluto e nem compensa integralmente diferenças estruturais das classes.\n",
    "* O **`class_weight`** atua no **cálculo da função de custo**, obrigando a rede a “se preocupar” mais em acertar a classe menos frequente, mesmo que ela ainda seja minoritária após o augmentation.\n",
    "\n",
    "#### **Resumo**\n",
    "\n",
    "* **Augmentation** = Mais dados sintéticos + mais diversidade (anti-overfitting)\n",
    "* **Class weight** = Ajuste matemático do quanto o modelo deve “se preocupar” com erros em cada classe\n",
    "\n",
    "É **prática recomendada em Deep Learning** para problemas de detecção com poucos dados reais!\n",
    "\n",
    "Se quiser, posso montar um comentário técnico para usar no seu código, explicando esse racional — deseja isso?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9c08ad",
   "metadata": {},
   "source": [
    "## Criando nosso modelo CNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "71de5f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/efrainmpp/Documents/Work/BioAcustica/.venv/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "    # 1ª camada convolucional\n",
    "    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(128, 27, 1)),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Dropout(0.1),\n",
    "\n",
    "    # 2ª camada convolucional\n",
    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Dropout(0.1),\n",
    "\n",
    "    # 3ª camada convolucional (opcional para melhor extração)\n",
    "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
    "    BatchNormalization(),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Dropout(0.1),\n",
    "\n",
    "    Flatten(),\n",
    "\n",
    "    # Camada densa\n",
    "    Dense(128, activation='relu'),\n",
    "    Dropout(0.1),\n",
    "\n",
    "    # Camada de saída\n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compilação\n",
    "model.compile(\n",
    "    optimizer=Adam(learning_rate=1e-4),\n",
    "    loss='binary_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e5dc8785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_15          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">27</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)    │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_16          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_17          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6144</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">786,560</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m32\u001b[0m)    │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_15          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m27\u001b[0m, \u001b[38;5;34m32\u001b[0m)    │           \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_15 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_20 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_16          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_16 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_21 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_17          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │           \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_17 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_22 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6144\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m786,560\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_23 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">880,257</span> (3.36 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m880,257\u001b[0m (3.36 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">879,809</span> (3.36 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m879,809\u001b[0m (3.36 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> (1.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d92babc",
   "metadata": {},
   "source": [
    "## Treinando Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9b7ea117",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 43ms/step - accuracy: 0.5867 - loss: 0.9668 - val_accuracy: 0.6923 - val_loss: 0.6616\n",
      "Epoch 2/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - accuracy: 0.7447 - loss: 0.5744 - val_accuracy: 0.5577 - val_loss: 0.7285\n",
      "Epoch 3/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - accuracy: 0.7739 - loss: 0.4461 - val_accuracy: 0.5481 - val_loss: 0.6924\n",
      "Epoch 4/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.7957 - loss: 0.4127 - val_accuracy: 0.6635 - val_loss: 0.6743\n",
      "Epoch 5/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.7979 - loss: 0.4042 - val_accuracy: 0.6923 - val_loss: 0.6379\n",
      "Epoch 6/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.8318 - loss: 0.3841 - val_accuracy: 0.7019 - val_loss: 0.5979\n",
      "Epoch 7/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - accuracy: 0.8268 - loss: 0.3258 - val_accuracy: 0.7115 - val_loss: 0.5811\n",
      "Epoch 8/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8587 - loss: 0.2873 - val_accuracy: 0.7308 - val_loss: 0.5576\n",
      "Epoch 9/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.8380 - loss: 0.3154 - val_accuracy: 0.7596 - val_loss: 0.4925\n",
      "Epoch 10/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - accuracy: 0.8467 - loss: 0.2831 - val_accuracy: 0.7500 - val_loss: 0.5515\n",
      "Epoch 11/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8239 - loss: 0.3071 - val_accuracy: 0.8365 - val_loss: 0.4405\n",
      "Epoch 12/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8879 - loss: 0.2334 - val_accuracy: 0.7788 - val_loss: 0.5320\n",
      "Epoch 13/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8458 - loss: 0.2782 - val_accuracy: 0.8077 - val_loss: 0.4360\n",
      "Epoch 14/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8519 - loss: 0.2613 - val_accuracy: 0.7788 - val_loss: 0.4862\n",
      "Epoch 15/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.8729 - loss: 0.2671 - val_accuracy: 0.7885 - val_loss: 0.4088\n",
      "Epoch 16/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.8847 - loss: 0.2420 - val_accuracy: 0.7788 - val_loss: 0.5360\n",
      "Epoch 17/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9121 - loss: 0.1966 - val_accuracy: 0.7788 - val_loss: 0.5447\n",
      "Epoch 18/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.8932 - loss: 0.2324 - val_accuracy: 0.7885 - val_loss: 0.5047\n",
      "Epoch 19/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8971 - loss: 0.2196 - val_accuracy: 0.7885 - val_loss: 0.4986\n",
      "Epoch 20/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9126 - loss: 0.1972 - val_accuracy: 0.7981 - val_loss: 0.5540\n",
      "Epoch 21/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.8990 - loss: 0.2190 - val_accuracy: 0.8173 - val_loss: 0.5284\n",
      "Epoch 22/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9084 - loss: 0.2049 - val_accuracy: 0.8462 - val_loss: 0.4009\n",
      "Epoch 23/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9364 - loss: 0.1716 - val_accuracy: 0.7788 - val_loss: 0.5784\n",
      "Epoch 24/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.8607 - loss: 0.2580 - val_accuracy: 0.8654 - val_loss: 0.3850\n",
      "Epoch 25/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.9219 - loss: 0.1819 - val_accuracy: 0.8654 - val_loss: 0.4690\n",
      "Epoch 26/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9322 - loss: 0.1740 - val_accuracy: 0.8173 - val_loss: 0.6131\n",
      "Epoch 27/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9158 - loss: 0.1962 - val_accuracy: 0.9038 - val_loss: 0.4524\n",
      "Epoch 28/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.8995 - loss: 0.2065 - val_accuracy: 0.9135 - val_loss: 0.4127\n",
      "Epoch 29/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.9175 - loss: 0.1812 - val_accuracy: 0.8654 - val_loss: 0.4551\n",
      "Epoch 30/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9073 - loss: 0.1855 - val_accuracy: 0.9231 - val_loss: 0.4464\n",
      "Epoch 31/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9398 - loss: 0.1692 - val_accuracy: 0.9231 - val_loss: 0.4342\n",
      "Epoch 32/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9382 - loss: 0.1404 - val_accuracy: 0.9231 - val_loss: 0.4594\n",
      "Epoch 33/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.8980 - loss: 0.1606 - val_accuracy: 0.8846 - val_loss: 0.4961\n",
      "Epoch 34/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9320 - loss: 0.1291 - val_accuracy: 0.9038 - val_loss: 0.4523\n",
      "Epoch 35/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9320 - loss: 0.1364 - val_accuracy: 0.9135 - val_loss: 0.5267\n",
      "Epoch 36/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9718 - loss: 0.1135 - val_accuracy: 0.9423 - val_loss: 0.3380\n",
      "Epoch 37/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9624 - loss: 0.1124 - val_accuracy: 0.9423 - val_loss: 0.3781\n",
      "Epoch 38/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9470 - loss: 0.1237 - val_accuracy: 0.9423 - val_loss: 0.4037\n",
      "Epoch 39/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.9612 - loss: 0.1155 - val_accuracy: 0.9327 - val_loss: 0.3558\n",
      "Epoch 40/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9772 - loss: 0.0841 - val_accuracy: 0.8942 - val_loss: 0.4526\n",
      "Epoch 41/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9673 - loss: 0.0938 - val_accuracy: 0.8942 - val_loss: 0.4988\n",
      "Epoch 42/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9564 - loss: 0.1005 - val_accuracy: 0.9231 - val_loss: 0.4863\n",
      "Epoch 43/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.9850 - loss: 0.0680 - val_accuracy: 0.9327 - val_loss: 0.4212\n",
      "Epoch 44/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9807 - loss: 0.0698 - val_accuracy: 0.9327 - val_loss: 0.4219\n",
      "Epoch 45/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9578 - loss: 0.0799 - val_accuracy: 0.9423 - val_loss: 0.4367\n",
      "Epoch 46/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.9830 - loss: 0.0593 - val_accuracy: 0.9327 - val_loss: 0.4657\n",
      "Epoch 47/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9927 - loss: 0.0446 - val_accuracy: 0.9519 - val_loss: 0.4242\n",
      "Epoch 48/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - accuracy: 0.9676 - loss: 0.0847 - val_accuracy: 0.8942 - val_loss: 0.5262\n",
      "Epoch 49/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - accuracy: 0.9608 - loss: 0.0851 - val_accuracy: 0.9423 - val_loss: 0.3945\n",
      "Epoch 50/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - accuracy: 0.9933 - loss: 0.0412 - val_accuracy: 0.9423 - val_loss: 0.4427\n",
      "Epoch 51/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - accuracy: 0.9747 - loss: 0.0586 - val_accuracy: 0.9423 - val_loss: 0.4052\n",
      "Epoch 52/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9762 - loss: 0.0636 - val_accuracy: 0.9327 - val_loss: 0.4590\n",
      "Epoch 53/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9971 - loss: 0.0321 - val_accuracy: 0.9519 - val_loss: 0.3966\n",
      "Epoch 54/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9798 - loss: 0.0637 - val_accuracy: 0.9327 - val_loss: 0.4981\n",
      "Epoch 55/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9868 - loss: 0.0297 - val_accuracy: 0.9327 - val_loss: 0.4093\n",
      "Epoch 56/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - accuracy: 0.9953 - loss: 0.0323 - val_accuracy: 0.9327 - val_loss: 0.4356\n",
      "Epoch 57/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.9968 - loss: 0.0233 - val_accuracy: 0.9327 - val_loss: 0.4686\n",
      "Epoch 58/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9755 - loss: 0.0540 - val_accuracy: 0.9519 - val_loss: 0.4361\n",
      "Epoch 59/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9883 - loss: 0.0454 - val_accuracy: 0.9135 - val_loss: 0.5287\n",
      "Epoch 60/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - accuracy: 0.9746 - loss: 0.0503 - val_accuracy: 0.9135 - val_loss: 0.5254\n",
      "Epoch 61/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9991 - loss: 0.0246 - val_accuracy: 0.9327 - val_loss: 0.5183\n",
      "Epoch 62/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 0.0178 - val_accuracy: 0.9519 - val_loss: 0.5350\n",
      "Epoch 63/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9933 - loss: 0.0241 - val_accuracy: 0.9423 - val_loss: 0.4804\n",
      "Epoch 64/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 0.0152 - val_accuracy: 0.9135 - val_loss: 0.5122\n",
      "Epoch 65/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9938 - loss: 0.0240 - val_accuracy: 0.9423 - val_loss: 0.5195\n",
      "Epoch 66/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9951 - loss: 0.0235 - val_accuracy: 0.9423 - val_loss: 0.4511\n",
      "Epoch 67/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - accuracy: 0.9865 - loss: 0.0322 - val_accuracy: 0.9423 - val_loss: 0.4891\n",
      "Epoch 68/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9948 - loss: 0.0206 - val_accuracy: 0.9327 - val_loss: 0.5522\n",
      "Epoch 69/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 0.9877 - loss: 0.0238 - val_accuracy: 0.9231 - val_loss: 0.6139\n",
      "Epoch 70/70\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 0.0091 - val_accuracy: 0.9519 - val_loss: 0.4838\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_split=0.2,\n",
    "    epochs=70,\n",
    "    batch_size=16,\n",
    "    class_weight=class_weights_dict,\n",
    "    verbose=1,\n",
    "    shuffle=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d8771f",
   "metadata": {},
   "source": [
    "## Testando Modelo e suas Métricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5697ac08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 11 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x3163f9940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99        72\n",
      "           1       0.98      0.98      0.98        59\n",
      "\n",
      "    accuracy                           0.98       131\n",
      "   macro avg       0.98      0.98      0.98       131\n",
      "weighted avg       0.98      0.98      0.98       131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Predição\n",
    "y_pred_prob = model.predict(X_test).ravel()\n",
    "y_pred = (y_pred_prob > 0.5).astype(int)\n",
    "\n",
    "# Relatório e Confusão\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e32038",
   "metadata": {},
   "source": [
    "## Plot dos Resultados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a8e26d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pFl0bAAAAJ6EiAQCA0yz3fm8nSAAA4DTLvUHCve8MAAA4jooEAABOs9w72JIgAQCA0yz3dgAQJAAAcJrl3oqEeyMSAABwHBUJAACcZrn3eztBAgAAp1l0bQAAAJyEigQAAA6zXFyRIEgAAOAwy8VBgq4NAABgGxUJAACcZolrESQAAHCYRdcGAADAyQgSAACchYqEFYClsrZv3y633HKLxMbGSmRkpPz2t7+V5cuX+7Z7vV4ZN26cNGjQwGxPTk6WDRs2VOoYBAkAAFwYJH755Rfp2LGjhIWFyWeffSZr166V5557Ts4//3zfPk8//bRMnDhRpk6dKkuXLpWoqChJSUmRgoKCCh+HMRIAALhwjMRTTz0ljRo1kszMTN+6pk2b+lUjXnjhBXnooYekd+/eZt1bb70lcXFxMnv2bBk0aFCFjkNFAgCAKqKwsFAOHDjgt+i6ssyZM0fatm0r/fv3l/r168vll18ur732mm/75s2bJTc313RnFIuJiZF27dpJdnZ2hdtEkAAAwGlWYJaMjAxzsi+56Lqy/PTTTzJlyhRp1qyZzJs3T+655x4ZNWqUvPnmm2a7hgilFYiS9HnxtoqgawMAgCrStZGeni5jxozxWxceHl7mvkVFRaYi8eSTT5rnWpFYvXq1GQ8xZMgQCRQqEgAAVBHh4eESHR3tt5QXJHQmxiWXXOK3rmXLlrJlyxbzOD4+3vzMy8vz20efF2+rCIIEAAAunLXRsWNHWb9+vd+6H3/8URo3buwbeKmBISsry7ddx1zo7I2kpKQKH4euDQAAXDhrIy0tTTp06GC6NgYMGCDffvutvPrqq2YpbtPo0aPl8ccfN+MoNFg8/PDD0rBhQ+nTp0+Fj0OQAADAha688kqZNWuWGVfx2GOPmaCg0z0HDx7s22fs2LFy+PBhufPOOyU/P186deokn3/+uURERFT4OJZXJ5K6TOTl9wW7CUBI+mXZpGA3AQg5EWfhK3Xsbe8H5HX2vnWzhBoqEgAAOM0S12KwJQAAsI2KBAAADrNcfBtxggQAAA6zCBIAAMAuy8VBgjESAADANioSAAA4zRLXIkgAAOAwi64NAACAk1GRAADAYZaLKxIECQAAHGa5OEjQtQEAAGyjIgEAgMMsF1ckCBIAADjNEteiawMAANhGRQIAAIdZdG0AAAC7LIIEAACwy3JxkGCMBAAAsI2KBAAATrPEtQgSAAA4zKJrAwAA4GRUJHDG1v2//5PGDWNPWj/1w4WSNv4jub1vRxnYs61c1uICia4ZKfGdH5T9h34NSluBYMpZvkymvzFNfli7Wnbv3i0TJr4s3bonB7tZOAssF1ckCBI4Y51ueUaqef73j+SSixrKp1NTZeb878zz8yLCZP7itWb526jeQWwpEFy//npEmjdvLn369pMx998X7ObgLLIIEkD59vxyyO/5A8NayaYtu+XrnA3m+aT3vjI/Oyc2C0r7gFDRqfPVZgHcJKhBYs+ePfLGG29Idna25ObmmnXx8fHSoUMHGTp0qNSrVy+YzYMNYdWryaDfXSkT3/lXsJsCACHDcnFFImiDLZctWyYXX3yxTJw4UWJiYqRLly5m0ce6rkWLFrJ8+fJgNQ823dC1tdSuFSnvzF0a7KYAQOiwArSEoKBVJFJTU6V///4yderUk5Ka1+uVu+++2+yj1YpTKSwsNIvf7xedEMtTzZF249SG9Okg875ZKzt37w92UwAAbq5IfP/995KWllZmuUfX6baVK1ee9nUyMjJMFaPkcjwvx6FW41QSGpwv3do1l+mzFwe7KQAQUizLCsgSioIWJHQsxLffflvudt0WFxd32tdJT0+X/fv3+y3V4xID3FpUxK03JMmufQfls6/XBLspABBSLBcHiaB1bTzwwANy5513Sk5OjnTv3t0XGvLy8iQrK0tee+01efbZZ0/7OuHh4WYpiW6Ns0//gt/Wu728+8lSOXGiyG9bXGwtiYuNlt8k1DXPWzVrKAcPF8jW3F/klwNHgtRi4Ow7cviwbNmyxfd8+7Ztsu6HH0wltUHDhkFtG5xlhWYGqNpBYuTIkVK3bl2ZMGGCTJ48WU6cOGHWV6tWTRITE2X69OkyYMCAYDUPlaRdGgkN6sibs5ectO2OmzrLQ3f/zvf8izfSzM8R495mUCbOKWvWrJY7ht3me/7s0xnm5w29b5S/PTk+iC0D7LO8OrIxyI4dO2amgioNF2FhYWf0epGXc6EXoCy/LJsU7CYAISfiLHylbvbg5wF5nQ3PXCehJiQuSKXBoUGDBsFuBgAAjrBc3LXBTbsAAEDVrkgAAOBmlotLEgQJAAAcZrk3R9C1AQAA7KMiAQCAwzwe95YkCBIAADjMcm+OoGsDAADYR0UCAACHWS4uSVCRAADAYZYVmKUyHn300ZNu+tWiRQvf9oKCAnO7itjYWKlZs6b069fP3O+qsggSAAC49O6fl156qezcudO3LFq0yLctLS1N5s6dKzNmzJAFCxbIjh07pG/fvpU+Bl0bAAC4VPXq1SU+Pv6k9fv375dp06bJe++9J926dTPrMjMzpWXLlrJkyRJp3759hY9BRQIAgCpSkSgsLJQDBw74LbquPBs2bJCGDRvKhRdeKIMHD/bdxj4nJ8fcMDM5Odm3r3Z7JCQkSHZ2dqXeG0ECAIAqMkYiIyNDYmJi/BZdV5Z27drJ9OnT5fPPP5cpU6bI5s2bpXPnznLw4EHJzc2VGjVqSO3atf1+Jy4uzmyrDLo2AACoItLT02XMmDF+68LDw8vct2fPnr7HrVu3NsGicePG8tFHH0lkZGTA2kSQAACgikz/DA8PLzc4nI5WHy6++GLZuHGj9OjRQ44ePSr5+fl+VQmdtVHWmIpToWsDAAAXTv8s7dChQ7Jp0yZp0KCBJCYmSlhYmGRlZfm2r1+/3oyhSEpKksqgIgEAgAs98MAD0qtXL9OdoVM7H3nkEalWrZrcfPPNZmzF8OHDTTdJnTp1JDo6WlJTU02IqMyMDUWQAADAhVe23LZtmwkNe/fulXr16kmnTp3M1E59rCZMmCAej8dciEpnfqSkpMjkyZMrfRzL6/V6xWUiL78v2E0AQtIvyyYFuwlAyIk4C1+p2z7+ZUBeZ/lDXSXUMEYCAADYRtcGAAAOs1x80y6CBAAADrPcmyMIEgAAOM1ycZJgjAQAALCNigQAAA6z3FuQIEgAAOA0y8VJgq4NAABgGxUJAAAcZrm3IEGQAADAaZaLkwRdGwAAwDYqEgAAOMxyb0GCIAEAgNMsFycJujYAAIBtVCQAAHCY5eKKBEECAACHWe7NEQQJAACcZrk4STBGAgAA2EZFAgAAh1nuLUgQJAAAcJrl4iRB1wYAALCNigQAAA6z3FuQIEgAAOA0j4uTBF0bAADANioSAAA4zHJvQYIgAQCA0ywXJwmCBAAADvO4N0cwRgIAANhHRQIAAIdZdG0AAAC7LPfmCLo2AACAfVQkAABwmCXuLUkQJAAAcJjHvTmCrg0AAGAfFQkAABxmuXi0JUECAACHWe7NEXRtAAAA+6hIAADgMI+LSxIECQAAHGa5N0cQJAAAcJrl4iTBGAkAAGAbFQkAABxmubcgQUUCAICzMdjSE4DlTIwfP950sYwePdq3rqCgQEaOHCmxsbFSs2ZN6devn+Tl5VXuvZ1RqwAAQMhbtmyZvPLKK9K6dWu/9WlpaTJ37lyZMWOGLFiwQHbs2CF9+/at1GsTJAAAcJgVoMWOQ4cOyeDBg+W1116T888/37d+//79Mm3aNHn++eelW7dukpiYKJmZmbJ48WJZsmRJhV+fIAEAgMMsywrIUlhYKAcOHPBbdN2paNfF9ddfL8nJyX7rc3Jy5NixY37rW7RoIQkJCZKdnV3h90aQAACgisjIyJCYmBi/RdeV54MPPpAVK1aUuU9ubq7UqFFDateu7bc+Li7ObKsoZm0AAFBFbiOenp4uY8aM8VsXHh5e5r5bt26V+++/X+bPny8RERHiFIIEAABV5IJU4eHh5QaH0rTrYteuXXLFFVf41p04cUIWLlwokyZNknnz5snRo0clPz/fryqhszbi4+Mr3CaCBAAALtS9e3dZtWqV37phw4aZcRB/+tOfpFGjRhIWFiZZWVlm2qdav369bNmyRZKSkip8HIIEAAAuvCBVrVq1pFWrVn7roqKizDUjitcPHz7cdJXUqVNHoqOjJTU11YSI9u3bV/g4BAkAAM7Re21MmDBBPB6PqUjo7I+UlBSZPHlypV7D8nq9XnGZyMvvC3YTgJD0y7JJwW4CEHIizsJX6qHv/zsgrzP9Zv8LSoUCpn8CAADb6NoAAOAc7doIBIIEAAAOs8S9KhwkKnMTj5kzZ9ptDwAAcGOQ0MtwAgCAyvPQtSHmjmAAAKDyLPfmCGZtAACAIAy2/Pvf/y4fffSRuZSmXqu7JL3TGAAAcP+sDVsViYkTJ5rrdeutRr/77ju56qqrzCU3f/rpJ+nZs2fgWwkAQBVmWYFZXBMk9PKZr776qrz00kvmXuZjx441tykdNWqU7N+/P/CtBAAA7gkS2p3RoUMH8zgyMlIOHjxoHt96663y/vvvB7aFAAC4YNaGJwCLa4KE3qd837595nFCQoIsWbLEPN68ebO48NYdAACcEYuuDX/dunWTOXPmmMc6ViItLU169OghAwcOlBtvvDHQbQQAoMoPtrQCsLhm1oaOjygqKjKPR44caQZaLl68WG644Qa56667At1GAAAQolx5G/GC48FuARCaGo34MNhNAELO7syBjh8jddYPAXmdl25sKa65INXXX38tt9xyiyQlJcn27dvNurffflsWLVoUyPYBAFDlWS7u2rAVJD7++GNJSUkxMzb0OhKFhYVmvU79fPLJJwPdRgAAEKJsBYnHH39cpk6dKq+99pqEhYX51nfs2JGrWgIAUIrHCszimsGW69evly5dupR5h9D8/PxAtAsAANfwhGgICOp1JDZu3HjSeh0fceGFFwaiXQAAwK1BYsSIEXL//ffL0qVLzeCPHTt2yLvvvit//OMf5Z577gl8KwEAqMIsFw+2tNW18ec//9lcR6J79+5y5MgR080RHh4uDz74oNxxxx2BbyUAAFWYJzQzQPAqEpqK/vrXv5rLZK9evdpcInv37t1mjETTpk0D30oAAFD1g4RO80xPT5e2bduaGRqffvqpXHLJJbJmzRpp3ry5vPjii+Zy2QAA4Ny410alujbGjRsnr7zyiiQnJ5tLYvfv39/ca0MrEs8995x5Xq1aNedaCwBAFeQJ1RRwtoPEjBkz5K233jL31NAujdatW8vx48fl+++/D9lBIAAAVNnLSLvtvW3btk0SExPN41atWpkBltqVQYgAAODcVKmKxIkTJ6RGjRr/++Xq1aVmzZpOtAsAANewXPx9u1JBQm8UOnToUFOJUAUFBXL33XdLVFSU334zZ84MbCsBAKjCPC5OEpUKEkOGDPF7rnf/BAAA565KBYnMzEznWgIAgEtZ7i1I2LuyJQAAqDiubAkAAFAGKhIAADjM4+K+DYIEAAAOs9ybI+jaAAAA9lGRAADAYR4XVyQIEgAAOMwS9yYJggQAAA7zuDdHMEYCAADYR0UCAACHeahIAAAAuyzLCshSGVOmTJHWrVtLdHS0WZKSkuSzzz7zbdcbb44cOVJiY2PNnbz79esneXl5lX5vBAkAAFzoggsukPHjx0tOTo4sX75cunXrJr1795Y1a9aY7WlpaTJ37lyZMWOGLFiwQHbs2CF9+/at9HHo2gAAwIVdG7169fJ7/sQTT5gqxZIlS0zImDZtmrz33nsmYBTfmLNly5Zme/v27St8HCoSAAA4zLICs9h14sQJ+eCDD+Tw4cOmi0OrFMeOHZPk5GTfPi1atJCEhATJzs6u1GtTkQAAoIooLCw0S0nh4eFmKcuqVatMcNDxEDoOYtasWXLJJZfIypUrpUaNGlK7dm2//ePi4iQ3N7dSbaIiAQDAWbhplycAS0ZGhsTExPgtuq48zZs3N6Fh6dKlcs8998iQIUNk7dq1AX1vVCQAAKgiYyTS09NlzJgxfuvKq0YorTpcdNFF5nFiYqIsW7ZMXnzxRRk4cKAcPXpU8vPz/aoSOmsjPj6+Um2iIgEAQBURHh7um85ZvJwqSJRWVFRkukY0VISFhUlWVpZv2/r162XLli2mK6QyqEgAAODC24inp6dLz549zQDKgwcPmhkaX331lcybN890iQwfPtxUN+rUqWMCSWpqqgkRlZmxoQgSAAA4zBOEm3bt2rVLbrvtNtm5c6cJDnpxKg0RPXr0MNsnTJggHo/HXIhKqxQpKSkyefLkSh/H8nq9XnGZguPBbgEQmhqN+DDYTQBCzu7MgY4fY/Li/wTkde7t0ERCDWMkAACAbXRtAADgMI+Lb9pFkAAAwGGeYIy2PEvo2gAAALZRkQAAwGGWewsSBAkAAJzmcXGSoGsDAADYRkUCAACHWe4tSBAkAABwmkfcy83vDQAAOIyKBAAADrNc3LdBkAAAwGGWuBdBAgAAh3lcXJFgjAQAALCNigQAAA6zxL0IEgAAOMxycZKgawMAANhGRQIAAIdZLi5JECQAAHCYR9zLze8NAAA4jIoEAAAOs+jaAAAAdlniXnRtAAAA26hIAADgMIuuDQAAYJdH3IsgAQCAwywXVyTcHJIAAIDDqEgAAOAwS9yLIAEAgMMsFycJujYAAIBtVCQAAHCYx8WdGwQJAAAcZrk3R9C1AQAA7KMiAQCAwyy6NgAAgF2We3MEXRsAAMA+KhIAADjMQ9cGAACwy3JvjiBIAADgNMvFQYIxEgAAwDYqEgAAOMxijAQAALDL494cQdcGAABulJGRIVdeeaXUqlVL6tevL3369JH169f77VNQUCAjR46U2NhYqVmzpvTr10/y8vIqdRyCBAAAZ6FrwwrAf5WxYMECExKWLFki8+fPl2PHjsm1114rhw8f9u2TlpYmc+fOlRkzZpj9d+zYIX379q3ce/N6vV5xmYLjwW4BEJoajfgw2E0AQs7uzIGOH+PL9XsD8jpdm8fa/t3du3ebyoQGhi5dusj+/fulXr168t5778lNN91k9lm3bp20bNlSsrOzpX379hV6XSoSAABUEYWFhXLgwAG/RddVhAYHVadOHfMzJyfHVCmSk5N9+7Ro0UISEhJMkKgoggQAAFWkayMjI0NiYmL8Fl13OkVFRTJ69Gjp2LGjtGrVyqzLzc2VGjVqSO3atf32jYuLM9sqilkbAABUkVkb6enpMmbMGL914eHhp/09HSuxevVqWbRokQQaQQIAgCoiPDy8QsGhpPvuu08++eQTWbhwoVxwwQW+9fHx8XL06FHJz8/3q0rorA3dVlEECTgiZ/kymf7GNPlh7WozwGfCxJelW/f/9cMBbvdg70tlbJ//lpCLbdh5QDr85TPzuH50hDwysI1cc2mcREWEyabcgzJh7lr5JGdbkFoMt12Qyuv1SmpqqsyaNUu++uoradq0qd/2xMRECQsLk6ysLDPtU+n00C1btkhSUlKFj0OQgCN+/fWING/eXPr07Sdj7r8v2M0BguKHbfvlpme+8j0/XlTkezxpRDuJOS9Mbnlxkew7VCj92jeW1+9Nkh7/N19WbckPUovhpnttjBw50szI+Mc//mGuJVE87kHHVURGRpqfw4cPN10lOgAzOjraBA8NERWdsaEIEnBEp85XmwU4l50oKpJdBwrK3HbVRbHy4Fs58t3mfeb583PXyl3XXixtmtQhSLiQFYRjTpkyxfy85ppr/NZnZmbK0KFDzeMJEyaIx+MxFQmd/ZGSkiKTJ0+u1HEIEgDgkKZxtWTV8zdIwbETsnzTXnn87/+W7fuOmG3fbtwrfa5KkPn/3in7jxyV3lc2kvCwavLNul3BbjZcwluBy0RFRETIyy+/bBa7Qnr659atW+X22293bE4tADhlxU97ZdTrS2Xg8wtk7Ns5klAvSuamd5OoiP9+f7tj8mIJq27Jhkk3yvZX+8tzQ9rK0JcWyeZdh4LddDjAY1kBWUJRSAeJffv2yZtvvnnKfcqaU/vMU6efUwsATspalStzlm+Ttdv2y5erc+Xm5xeaMRF9rmxktqf3/a1ER9aQvk9/KT0emy9T/vmjvH5vB2l5QUywmw4HWAFaQlFQuzbmzJlzyu0//fSTrTm13mqVmxoDAE478Osx2ZR3SJrG1ZQm9aLkjuRm0umvn8n6HQfM9jVb86V9s7pye7eLzNgJoKoIapDQO5FZlnXKfhzdXtk5tdxrA0CoiQqvbgLEjPwCiQz/7//1FpX6/z59Hqrla5whS1wrqF0bDRo0kJkzZ5pLd5a1rFixIpjNwxk4cviwrPvhB7Oo7du2mcc7d+wIdtOAs+LRgW2kQ/N60ij2PLnyoliZntpRTni9MnPpFnM9iZ/yDppxEZc3rWMCxj0pzeXqS+Lls++2B7vpcMndP8+JioReDENvGtK7d+8yt5+uWoHQtWbNarlj2G2+588+/d9xKzf0vlH+9uT4ILYMODsann+evHJXkpxfs4bsPVgoSzfskZ5/+8I8VjdPWCgP39Ra3rm/sxmAuTnvkNz3+lL54t87g910oOrcRvzrr78290W/7rrrytyu25YvXy5XX1256xHQtQGUjduIA8G5jfi3P/33zptn6qoLQ28wblArEp07dz7l9qioqEqHCAAAQo0l7hXS0z8BAEBo48qWAAA4zRLXIkgAAOAwy8VJgiABAIDDLPfmCMZIAAAA+6hIAADgMEvciyABAIDTLHEtujYAAIBtVCQAAHCY5eKSBEECAACHWe7NEXRtAAAA+6hIAADgMEvciyABAIDTLHEtujYAAIBtVCQAAHCY5eKSBEECAACHWe7NEQQJAACcZol7MUYCAADYRkUCAACnWeJaBAkAABxmuThJ0LUBAABsoyIBAIDDLPcWJAgSAAA4zRL3omsDAADYRkUCAACnWeJaBAkAABxmuThJ0LUBAABsoyIBAIDDLPcWJAgSAAA4zRL3IkgAAOA0S1yLMRIAAMA2KhIAADjMcnFJgiABAIDDLPfmCLo2AACAfQQJAAAcZgVoqayFCxdKr169pGHDhmJZlsyePdtvu9frlXHjxkmDBg0kMjJSkpOTZcOGDZU6BkECAACXJonDhw9LmzZt5OWXXy5z+9NPPy0TJ06UqVOnytKlSyUqKkpSUlKkoKCgwsdgjAQAAC7Vs2dPs5RFqxEvvPCCPPTQQ9K7d2+z7q233pK4uDhTuRg0aFCFjkFFAgCAszBrwwrAf4WFhXLgwAG/RdfZsXnzZsnNzTXdGcViYmKkXbt2kp2dXeHXIUgAAHAWZm1YAVgyMjLMyb7kouvs0BChtAJRkj4v3lYRdG0AAFBFpKeny5gxY/zWhYeHSzARJAAAcJgVoNfR0BCo4BAfH29+5uXlmVkbxfT5ZZddVuHXoWsDAAC3zv88haZNm5owkZWV5VunYy509kZSUpJUFBUJAABceonsQ4cOycaNG/0GWK5cuVLq1KkjCQkJMnr0aHn88celWbNmJlg8/PDD5poTffr0qfAxCBIAALjU8uXLpWvXrr7nxeMrhgwZItOnT5exY8eaa03ceeedkp+fL506dZLPP/9cIiIiKnwMy6sTSV2m4HiwWwCEpkYjPgx2E4CQsztzoOPH2LLP3hTN0hLqBHdgZVmoSAAA4DBL3IvBlgAAwDYqEgAAOMxycUmCIAEAgOMscSu6NgAAgG1UJAAAcJjl3oIEQQIAAKdZ4l50bQAAANuoSAAA4DDLxSUJggQAAC6918bZQJAAAMBplrgWYyQAAIBtVCQAAHCYJe5FkAAAwGGWi5MEXRsAAMA2KhIAADjMcnHnBkECAACnWeJadG0AAADbqEgAAOAwS9yLIAEAgMMsFycJujYAAIBtVCQAAHCY5eLODYIEAAAOs9ybI+jaAAAA9hEkAACAbXRtAADgMMvFXRsECQAAHGa5eLAlXRsAAMA2KhIAADjMcm9BgiABAIDTLHEvujYAAIBtVCQAAHCaJa5FkAAAwGGWi5MEXRsAAMA2KhIAADjMcm9BgiABAIDTLHEvggQAAE6zxLUYIwEAAGyjIgEAgMMsF5ckCBIAADjMcm+OoGsDAADYZ3m9Xu8Z/D5QrsLCQsnIyJD09HQJDw8PdnOAkMG/DbgJQQKOOXDggMTExMj+/fslOjo62M0BQgb/NuAmdG0AAADbCBIAAMA2ggQAALCNIAHH6CCyRx55hMFkQCn824CbMNgSAADYRkUCAADYRpAAAAC2ESQAAIBtBAkAAGAbQQKOefnll6VJkyYSEREh7dq1k2+//TbYTQKCauHChdKrVy9p2LChWJYls2fPDnaTgDNGkIAjPvzwQxkzZoyZ4rZixQpp06aNpKSkyK5du4LdNCBoDh8+bP4taMgG3ILpn3CEViCuvPJKmTRpknleVFQkjRo1ktTUVPnzn/8c7OYBQacViVmzZkmfPn2C3RTgjFCRQMAdPXpUcnJyJDk52bfO4/GY59nZ2UFtGwAgsAgSCLg9e/bIiRMnJC4uzm+9Ps/NzQ1auwAAgUeQAAAAthEkEHB169aVatWqSV5ent96fR4fHx+0dgEAAo8ggYCrUaOGJCYmSlZWlm+dDrbU50lJSUFtGwAgsKoH+PUAQ6d+DhkyRNq2bStXXXWVvPDCC2bq27Bhw4LdNCBoDh06JBs3bvQ937x5s6xcuVLq1KkjCQkJQW0bYBfTP+EYnfr5zDPPmAGWl112mUycONFMCwXOVV999ZV07dr1pPUauqdPnx6UNgFniiABAABsY4wEAACwjSABAABsI0gAAADbCBIAAMA2ggQAALCNIAEAAGwjSAAAANsIEsA5aujQodKnTx/f82uuuUZGjx4d1DYBqHoIEkAInuAtyzKL3rfkoosukscee0yOHz/u6HFnzpwpf/vb33zPmzRpYi5tDgCnwr02gBB03XXXSWZmphQWFsqnn34qI0eOlLCwMElPT/fb7+jRoyZsBILe7wEAKouKBBCCwsPDzS3XGzduLPfcc48kJyfLnDlzfN0RTzzxhDRs2FCaN29u9t+6dasMGDBAateubQJB79695T//+Y/v9U6cOGFupKbbY2NjZezYsVL66vgluzb08c8//yxpaWm+6kixjz/+WC699FLTRq1aPPfcc2ftcwEQeggSQBUQGRlpqg9Kb8e+fv16mT9/vnzyySdy7NgxSUlJkVq1asnXX38t33zzjdSsWdNUNYp/R0/2elOoN954QxYtWiT79u2TWbNmnbKb44ILLjBdKjt37jSLysnJMYFl0KBBsmrVKnn00Ufl4Ycf5oZTwDmMrg0ghGnVQIPDvHnzJDU1VXbv3i1RUVHy+uuv+7o03nnnHSkqKjLriisH2i2i1Qe92+S1115rxjpot0jfvn3N9qlTp5rXLI9WNapVq2bCiVZGij3//PPSvXt3Ex7UxRdfLGvXrjV3edVqCYBzDxUJIARppUGrChEREdKzZ08ZOHCg+favfvvb3/qNi/j+++9l48aN5qSvv6OLBoGCggLZtGmT7N+/31QUSt7CvXr16tK2bdtKt+uHH36Qjh07+q3T5xs2bDDdJwDOPVQkgBDUtWtXmTJligkMOhZCT/zFtCJR0qFDhyQxMVHefffdk16nXr16Z6W9AM5dBAkgBGlY0GmfFXHFFVfIhx9+KPXr15fo6Ogy92nQoIEsXbpUunTpYp7rVFId76C/Wx4NMaWrDC1btjRjMErS59rFoV0hAM49dG0AVdzgwYOlbt26ZqaGDrbcvHmzGRsxatQo2bZtm9nn/vvvl/Hjx8vs2bNl3bp1cu+990p+fv4pX1dnZCxcuFC2b98ue/bsMev++Mc/mjEber2JH3/8Ud58802ZNGmSPPDAA2flvQIIPQQJoIo777zzzAk/ISHBDKbUqsHw4cPNGIniCoUGgFtvvVWGDBkiSUlJZjzFjTfeeMrX1RkbOoX0N7/5ja+LRCsYH330kXzwwQfSqlUrGTdunNmPgZbAucvylp5MDgAAUEFUJAAAgG0ECQAAYBtBAgAA2EaQAAAAthEkAACAbQQJAABgG0ECAADYRpAAAAC2ESQAAIBtBAkAAGAbQQIAANhGkAAAAGLX/wczXcB3Byz52gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d', cmap='Blues')\n",
    "plt.title(\"Matriz de Confusão\")\n",
    "plt.xlabel(\"Predito\")\n",
    "plt.ylabel(\"Real\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "347c01a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Curva ROC\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr, tpr, label=f\"AUC = {roc_auc:.2f}\")\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.title(\"Curva ROC\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "55aa50ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Supondo que você salvou 'history' do model.fit(...)\n",
    "plt.figure(figsize=(14, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Treino')\n",
    "plt.plot(history.history['val_loss'], label='Validação')\n",
    "plt.title('Loss durante o Treinamento')\n",
    "plt.xlabel('Época')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['accuracy'], label='Treino')\n",
    "plt.plot(history.history['val_accuracy'], label='Validação')\n",
    "plt.title('Acurácia durante o Treinamento')\n",
    "plt.xlabel('Época')\n",
    "plt.ylabel('Acurácia')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
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